• Nibras Dsouza
• Dr M. Lutful Islam
• Ansari Faisal Shahabuddin
• Shaikh Ayaan Imran Almas
• Shirsekar Arsh Mazhar

In the digital era, the abundance of available books necessitates effective recommendation systems to aid users in discovering content aligned with their preferences. This research presents a novel approach to book recommendation utilizing a hybrid filtering technique, amalgamating content-based filtering and collaborative filtering methodologies. The proposed system is implemented as an Android mobile application, offering users access to a comprehensive database comprising 32,000 books. By leveraging content-based filtering to analyze textual features and collaborative filtering to assess user preferences and behaviors, the hybrid system enhances recommendation accuracy and diversity. Through extensive experimentation and evaluation, our approach demonstrates superior performance compared to traditional single-method recommendation systems. The results underscore the efficacy and potential of hybrid filtering techniques in facilitating personalized book recommendations, thereby enriching user experiences, and fostering engagement with literary content.

Recommendation system, Content based, Collaborative filtering, KNN Algorithm

" Enhancing Literary Exploration: A Hybrid Filtering Approach for Personalized Book Recommendations", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.h279-h284, March-2024, Available :http://www.jetir.org/papers/JETIR2403737.pdf

" Enhancing Literary Exploration: A Hybrid Filtering Approach for Personalized Book Recommendations", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. pph279-h284, March-2024, Available at : http://www.jetir.org/papers/JETIR2403737.pdf